Overview

Dataset statistics

Number of variables16
Number of observations8626
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory128.0 B

Variable types

Text4
Numeric7
Categorical5

Alerts

VISION is highly overall correlated with VISCORR and 2 other fieldsHigh correlation
VISCORR is highly overall correlated with VISION and 2 other fieldsHigh correlation
HEARING is highly overall correlated with VISION and 2 other fieldsHigh correlation
HEARAID is highly overall correlated with VISION and 2 other fieldsHigh correlation
HEIGHT is highly skewed (γ1 = 53.11104541)Skewed
BPDIAS is highly skewed (γ1 = 26.05957215)Skewed
OASIS_session_label has unique valuesUnique
days_to_visit has 1355 (15.7%) zerosZeros

Reproduction

Analysis started2023-10-18 04:52:48.522612
Analysis finished2023-10-18 04:52:56.640314
Duration8.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct1378
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:56.871671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69008
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)1.5%

Sample

1st rowOAS30001
2nd rowOAS30001
3rd rowOAS30001
4th rowOAS30001
5th rowOAS30001
ValueCountFrequency (%)
oas30446 32
 
0.4%
oas30936 31
 
0.4%
oas30675 30
 
0.3%
oas30393 30
 
0.3%
oas31155 28
 
0.3%
oas30194 28
 
0.3%
oas30314 26
 
0.3%
oas31160 25
 
0.3%
oas31100 25
 
0.3%
oas30825 24
 
0.3%
Other values (1368) 8347
96.8%
2023-10-18T10:22:57.221694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11332
16.4%
0 9732
14.1%
O 8626
12.5%
A 8626
12.5%
S 8626
12.5%
1 4917
7.1%
2 2658
 
3.9%
7 2585
 
3.7%
4 2524
 
3.7%
5 2518
 
3.6%
Other values (3) 6864
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43130
62.5%
Uppercase Letter 25878
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11332
26.3%
0 9732
22.6%
1 4917
11.4%
2 2658
 
6.2%
7 2585
 
6.0%
4 2524
 
5.9%
5 2518
 
5.8%
8 2345
 
5.4%
6 2315
 
5.4%
9 2204
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
O 8626
33.3%
A 8626
33.3%
S 8626
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43130
62.5%
Latin 25878
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11332
26.3%
0 9732
22.6%
1 4917
11.4%
2 2658
 
6.2%
7 2585
 
6.0%
4 2524
 
5.9%
5 2518
 
5.8%
8 2345
 
5.4%
6 2315
 
5.4%
9 2204
 
5.1%
Latin
ValueCountFrequency (%)
O 8626
33.3%
A 8626
33.3%
S 8626
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11332
16.4%
0 9732
14.1%
O 8626
12.5%
A 8626
12.5%
S 8626
12.5%
1 4917
7.1%
2 2658
 
3.9%
7 2585
 
3.7%
4 2524
 
3.7%
5 2518
 
3.6%
Other values (3) 6864
9.9%

OASIS_session_label
Text

UNIQUE 

Distinct8626
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:57.404881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length20
Mean length20.003014
Min length20

Characters and Unicode

Total characters172546
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8626 ?
Unique (%)100.0%

Sample

1st rowOAS30001_UDSb1_d0000
2nd rowOAS30001_UDSb1_d0339
3rd rowOAS30001_UDSb1_d0722
4th rowOAS30001_UDSb1_d1106
5th rowOAS30001_UDSb1_d1456
ValueCountFrequency (%)
oas30001_udsb1_d0000 1
 
< 0.1%
oas30002_udsb1_d1850 1
 
< 0.1%
oas30001_udsb1_d1894 1
 
< 0.1%
oas30001_udsb1_d2181 1
 
< 0.1%
oas30001_udsb1_d2699 1
 
< 0.1%
oas30001_udsb1_d3025 1
 
< 0.1%
oas30001_udsb1_d3332 1
 
< 0.1%
oas30001_udsb1_d3675 1
 
< 0.1%
oas30001_udsb1_d3977 1
 
< 0.1%
oas30001_udsb1_d4349 1
 
< 0.1%
Other values (8616) 8616
99.9%
2023-10-18T10:22:57.706691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 18864
10.9%
1 17853
10.3%
S 17252
 
10.0%
_ 17252
 
10.0%
3 14664
 
8.5%
O 8626
 
5.0%
A 8626
 
5.0%
d 8626
 
5.0%
b 8626
 
5.0%
D 8626
 
5.0%
Other values (9) 43531
25.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 86281
50.0%
Uppercase Letter 51756
30.0%
Connector Punctuation 17252
 
10.0%
Lowercase Letter 17252
 
10.0%
Dash Punctuation 5
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18864
21.9%
1 17853
20.7%
3 14664
17.0%
2 6035
 
7.0%
4 5575
 
6.5%
5 5025
 
5.8%
7 5003
 
5.8%
8 4591
 
5.3%
6 4470
 
5.2%
9 4201
 
4.9%
Uppercase Letter
ValueCountFrequency (%)
S 17252
33.3%
O 8626
16.7%
A 8626
16.7%
D 8626
16.7%
U 8626
16.7%
Lowercase Letter
ValueCountFrequency (%)
d 8626
50.0%
b 8626
50.0%
Connector Punctuation
ValueCountFrequency (%)
_ 17252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103538
60.0%
Latin 69008
40.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18864
18.2%
1 17853
17.2%
_ 17252
16.7%
3 14664
14.2%
2 6035
 
5.8%
4 5575
 
5.4%
5 5025
 
4.9%
7 5003
 
4.8%
8 4591
 
4.4%
6 4470
 
4.3%
Other values (2) 4206
 
4.1%
Latin
ValueCountFrequency (%)
S 17252
25.0%
O 8626
12.5%
A 8626
12.5%
d 8626
12.5%
b 8626
12.5%
D 8626
12.5%
U 8626
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 172546
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18864
10.9%
1 17853
10.3%
S 17252
 
10.0%
_ 17252
 
10.0%
3 14664
 
8.5%
O 8626
 
5.0%
A 8626
 
5.0%
d 8626
 
5.0%
b 8626
 
5.0%
D 8626
 
5.0%
Other values (9) 43531
25.2%

days_to_visit
Real number (ℝ)

ZEROS 

Distinct3426
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2031.2152
Minimum-39520
Maximum12334
Zeros1355
Zeros (%)15.7%
Negative5
Negative (%)0.1%
Memory size67.5 KiB
2023-10-18T10:22:57.855010image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-39520
5-th percentile0
Q1469.75
median1510
Q33079
95-th percentile5619.25
Maximum12334
Range51854
Interquartile range (IQR)2609.25

Descriptive statistics

Standard deviation1951.0234
Coefficient of variation (CV)0.9605203
Kurtosis25.176248
Mean2031.2152
Median Absolute Deviation (MAD)1139
Skewness0.040577843
Sum17521262
Variance3806492.3
MonotonicityNot monotonic
2023-10-18T10:22:57.992866image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1355
 
15.7%
371 26
 
0.3%
364 19
 
0.2%
385 18
 
0.2%
378 18
 
0.2%
357 14
 
0.2%
1099 14
 
0.2%
406 14
 
0.2%
350 13
 
0.2%
392 13
 
0.2%
Other values (3416) 7122
82.6%
ValueCountFrequency (%)
-39520 1
 
< 0.1%
-101 1
 
< 0.1%
-15 1
 
< 0.1%
-2 1
 
< 0.1%
-1 1
 
< 0.1%
0 1355
15.7%
1 5
 
0.1%
2 2
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
ValueCountFrequency (%)
12334 1
< 0.1%
11849 1
< 0.1%
11723 1
< 0.1%
11639 1
< 0.1%
11504 1
< 0.1%
11493 1
< 0.1%
11303 1
< 0.1%
11066 1
< 0.1%
10928 1
< 0.1%
10711 1
< 0.1%

age at visit
Real number (ℝ)

Distinct3163
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.33844
Minimum-47.25
Maximum100.55
Zeros0
Zeros (%)0.0%
Negative1
Negative (%)< 0.1%
Memory size67.5 KiB
2023-10-18T10:22:58.125690image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-47.25
5-th percentile59.41
Q169.27
median74.425
Q379.91
95-th percentile88.16
Maximum100.55
Range147.8
Interquartile range (IQR)10.64

Descriptive statistics

Standard deviation8.6251294
Coefficient of variation (CV)0.11602516
Kurtosis4.9007513
Mean74.33844
Median Absolute Deviation (MAD)5.305
Skewness-0.58549591
Sum641243.38
Variance74.392857
MonotonicityNot monotonic
2023-10-18T10:22:58.239211image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.04 12
 
0.1%
70.75 12
 
0.1%
73.59 12
 
0.1%
73.78 11
 
0.1%
74.07 11
 
0.1%
68.75 11
 
0.1%
69.85 11
 
0.1%
75.18 11
 
0.1%
69.44 11
 
0.1%
67.53 11
 
0.1%
Other values (3153) 8513
98.7%
ValueCountFrequency (%)
-47.25 1
< 0.1%
42.5 1
< 0.1%
43.24 1
< 0.1%
43.5 1
< 0.1%
45.22 1
< 0.1%
45.24 1
< 0.1%
45.3 1
< 0.1%
45.52 1
< 0.1%
45.61 1
< 0.1%
45.66 2
< 0.1%
ValueCountFrequency (%)
100.55 1
< 0.1%
99.24 1
< 0.1%
98.95 1
< 0.1%
98.9 1
< 0.1%
98.73 1
< 0.1%
98.69 1
< 0.1%
98.34 1
< 0.1%
98.27 1
< 0.1%
97.98 1
< 0.1%
97.85 1
< 0.1%

WEIGHT
Real number (ℝ)

Distinct653
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean174.42991
Minimum67
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:58.400257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum67
5-th percentile116
Q1147
median174
Q3188
95-th percentile235
Maximum999
Range932
Interquartile range (IQR)41

Descriptive statistics

Standard deviation66.628796
Coefficient of variation (CV)0.38198033
Kurtosis98.55101
Mean174.42991
Median Absolute Deviation (MAD)21.5
Skewness8.5818893
Sum1504632.4
Variance4439.3965
MonotonicityNot monotonic
2023-10-18T10:22:58.523457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
174.4299121 1003
 
11.6%
184 106
 
1.2%
160 104
 
1.2%
180 101
 
1.2%
156 100
 
1.2%
172 91
 
1.1%
162 86
 
1.0%
166 85
 
1.0%
154 82
 
1.0%
178 82
 
1.0%
Other values (643) 6786
78.7%
ValueCountFrequency (%)
67 1
 
< 0.1%
74 2
< 0.1%
75.5 1
 
< 0.1%
78 2
< 0.1%
79 2
< 0.1%
81 2
< 0.1%
82 2
< 0.1%
83 1
 
< 0.1%
85 1
 
< 0.1%
88 3
< 0.1%
ValueCountFrequency (%)
999 26
0.3%
888 20
0.2%
326 1
 
< 0.1%
320 1
 
< 0.1%
317 1
 
< 0.1%
316 3
 
< 0.1%
315 1
 
< 0.1%
311 2
 
< 0.1%
310 1
 
< 0.1%
308 1
 
< 0.1%

HEIGHT
Real number (ℝ)

SKEWED 

Distinct234
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.693765
Minimum51
Maximum9999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:58.672739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum51
5-th percentile60.5
Q163.7
median67
Q370.693765
95-th percentile72
Maximum9999
Range9948
Interquartile range (IQR)6.9937651

Descriptive statistics

Standard deviation185.80043
Coefficient of variation (CV)2.6282435
Kurtosis2834.9196
Mean70.693765
Median Absolute Deviation (MAD)3.6937651
Skewness53.111045
Sum609804.42
Variance34521.798
MonotonicityNot monotonic
2023-10-18T10:22:58.789613image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70.69376512 1763
20.4%
64 427
 
5.0%
62 396
 
4.6%
63 385
 
4.5%
67 384
 
4.5%
65 346
 
4.0%
70 335
 
3.9%
66 328
 
3.8%
69 279
 
3.2%
68 277
 
3.2%
Other values (224) 3706
43.0%
ValueCountFrequency (%)
51 3
< 0.1%
52 4
< 0.1%
52.3 1
 
< 0.1%
52.4 1
 
< 0.1%
52.5 3
< 0.1%
52.7 1
 
< 0.1%
52.75 2
 
< 0.1%
53 6
0.1%
53.5 1
 
< 0.1%
54 4
< 0.1%
ValueCountFrequency (%)
9999 3
 
< 0.1%
999 2
 
< 0.1%
183 1
 
< 0.1%
99.9 10
 
0.1%
99 19
0.2%
88.8 37
0.4%
79.8 1
 
< 0.1%
79.5 1
 
< 0.1%
79.3 1
 
< 0.1%
79 7
 
0.1%

BPSYS
Real number (ℝ)

Distinct99
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.63416
Minimum2
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:58.938737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile105
Q1120
median130
Q3138
95-th percentile158
Maximum999
Range997
Interquartile range (IQR)18

Descriptive statistics

Standard deviation39.026423
Coefficient of variation (CV)0.29874592
Kurtosis380.5371
Mean130.63416
Median Absolute Deviation (MAD)10
Skewness17.916153
Sum1126850.3
Variance1523.0617
MonotonicityNot monotonic
2023-10-18T10:22:59.058200image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.6341624 1027
 
11.9%
120 723
 
8.4%
130 646
 
7.5%
140 498
 
5.8%
110 458
 
5.3%
118 440
 
5.1%
122 410
 
4.8%
138 374
 
4.3%
128 360
 
4.2%
132 320
 
3.7%
Other values (89) 3370
39.1%
ValueCountFrequency (%)
2 1
 
< 0.1%
85 1
 
< 0.1%
86 1
 
< 0.1%
88 4
 
< 0.1%
90 18
0.2%
92 5
 
0.1%
93 1
 
< 0.1%
94 9
0.1%
95 1
 
< 0.1%
96 13
0.2%
ValueCountFrequency (%)
999 10
0.1%
888 6
0.1%
408 1
 
< 0.1%
210 2
 
< 0.1%
202 1
 
< 0.1%
200 1
 
< 0.1%
198 2
 
< 0.1%
196 1
 
< 0.1%
194 3
 
< 0.1%
192 4
 
< 0.1%

BPDIAS
Real number (ℝ)

SKEWED 

Distinct61
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.256718
Minimum40
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:59.190295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile60
Q168
median74.256718
Q380
95-th percentile90
Maximum999
Range959
Interquartile range (IQR)12

Descriptive statistics

Standard deviation32.76825
Coefficient of variation (CV)0.44128331
Kurtosis732.91073
Mean74.256718
Median Absolute Deviation (MAD)5.7432824
Skewness26.059572
Sum640538.45
Variance1073.7582
MonotonicityNot monotonic
2023-10-18T10:22:59.354523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70 1132
13.1%
80 1049
12.2%
74.2567176 1034
12.0%
60 791
 
9.2%
78 577
 
6.7%
72 491
 
5.7%
82 425
 
4.9%
68 407
 
4.7%
64 407
 
4.7%
62 326
 
3.8%
Other values (51) 1987
23.0%
ValueCountFrequency (%)
40 3
 
< 0.1%
42 2
 
< 0.1%
44 2
 
< 0.1%
46 1
 
< 0.1%
48 2
 
< 0.1%
50 54
0.6%
52 16
 
0.2%
53 1
 
< 0.1%
54 24
0.3%
55 7
 
0.1%
ValueCountFrequency (%)
999 10
 
0.1%
118 1
 
< 0.1%
112 1
 
< 0.1%
110 1
 
< 0.1%
108 1
 
< 0.1%
106 2
 
< 0.1%
104 4
 
< 0.1%
102 5
 
0.1%
100 48
0.6%
99 1
 
< 0.1%

HRATE
Real number (ℝ)

Distinct69
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.481159
Minimum40
Maximum999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size67.5 KiB
2023-10-18T10:22:59.508000image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile56
Q164
median71.481159
Q372
95-th percentile84
Maximum999
Range959
Interquartile range (IQR)8

Descriptive statistics

Standard deviation45.793686
Coefficient of variation (CV)0.64063996
Kurtosis375.89767
Mean71.481159
Median Absolute Deviation (MAD)6.5188414
Skewness19.017899
Sum616596.47
Variance2097.0617
MonotonicityNot monotonic
2023-10-18T10:22:59.741134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
71.48115856 1859
21.6%
60 1117
12.9%
64 930
10.8%
72 837
9.7%
68 659
 
7.6%
80 571
 
6.6%
76 389
 
4.5%
56 328
 
3.8%
78 209
 
2.4%
84 185
 
2.1%
Other values (59) 1542
17.9%
ValueCountFrequency (%)
40 5
 
0.1%
41 1
 
< 0.1%
42 7
 
0.1%
44 13
 
0.2%
45 2
 
< 0.1%
46 9
 
0.1%
48 71
0.8%
49 2
 
< 0.1%
50 37
0.4%
51 5
 
0.1%
ValueCountFrequency (%)
999 17
0.2%
888 4
 
< 0.1%
272 1
 
< 0.1%
156 1
 
< 0.1%
120 1
 
< 0.1%
118 1
 
< 0.1%
112 1
 
< 0.1%
110 2
 
< 0.1%
108 2
 
< 0.1%
104 7
0.1%

VISION
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
1.0
4538 
0.0
2106 
0.8471525523753319
1848 
9.0
 
133
7.0
 
1

Length

Max length18
Median length3
Mean length6.2135405
Min length3

Characters and Unicode

Total characters53598
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 4538
52.6%
0.0 2106
24.4%
0.8471525523753319 1848
21.4%
9.0 133
 
1.5%
7.0 1
 
< 0.1%

Length

2023-10-18T10:22:59.886198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-18T10:23:00.062392image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 4538
52.6%
0.0 2106
24.4%
0.8471525523753319 1848
21.4%
9.0 133
 
1.5%
7.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 10732
20.0%
. 8626
16.1%
1 8234
15.4%
5 7392
13.8%
3 5544
10.3%
7 3697
 
6.9%
2 3696
 
6.9%
9 1981
 
3.7%
8 1848
 
3.4%
4 1848
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44972
83.9%
Other Punctuation 8626
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10732
23.9%
1 8234
18.3%
5 7392
16.4%
3 5544
12.3%
7 3697
 
8.2%
2 3696
 
8.2%
9 1981
 
4.4%
8 1848
 
4.1%
4 1848
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 8626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53598
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10732
20.0%
. 8626
16.1%
1 8234
15.4%
5 7392
13.8%
3 5544
10.3%
7 3697
 
6.9%
2 3696
 
6.9%
9 1981
 
3.7%
8 1848
 
3.4%
4 1848
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53598
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10732
20.0%
. 8626
16.1%
1 8234
15.4%
5 7392
13.8%
3 5544
10.3%
7 3697
 
6.9%
2 3696
 
6.9%
9 1981
 
3.7%
8 1848
 
3.4%
4 1848
 
3.4%

VISCORR
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
1.0
5524 
0.8347107438016529
1850 
0.0
1237 
9.0
 
14
6.0
 
1

Length

Max length18
Median length3
Mean length6.2170183
Min length3

Characters and Unicode

Total characters53628
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5524
64.0%
0.8347107438016529 1850
 
21.4%
0.0 1237
 
14.3%
9.0 14
 
0.2%
6.0 1
 
< 0.1%

Length

2023-10-18T10:23:00.174572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-18T10:23:00.322212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5524
64.0%
0.8347107438016529 1850
 
21.4%
0.0 1237
 
14.3%
9.0 14
 
0.2%
6.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13563
25.3%
1 9224
17.2%
. 8626
16.1%
8 3700
 
6.9%
3 3700
 
6.9%
4 3700
 
6.9%
7 3700
 
6.9%
9 1864
 
3.5%
6 1851
 
3.5%
5 1850
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45002
83.9%
Other Punctuation 8626
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13563
30.1%
1 9224
20.5%
8 3700
 
8.2%
3 3700
 
8.2%
4 3700
 
8.2%
7 3700
 
8.2%
9 1864
 
4.1%
6 1851
 
4.1%
5 1850
 
4.1%
2 1850
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 8626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53628
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13563
25.3%
1 9224
17.2%
. 8626
16.1%
8 3700
 
6.9%
3 3700
 
6.9%
4 3700
 
6.9%
7 3700
 
6.9%
9 1864
 
3.5%
6 1851
 
3.5%
5 1850
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53628
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13563
25.3%
1 9224
17.2%
. 8626
16.1%
8 3700
 
6.9%
3 3700
 
6.9%
4 3700
 
6.9%
7 3700
 
6.9%
9 1864
 
3.5%
6 1851
 
3.5%
5 1850
 
3.4%

VISWCORR
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
1.0
5188 
1.0812145600877996
3159 
0.0
 
198
9.0
 
80
3.0
 
1

Length

Max length18
Median length3
Mean length8.4932761
Min length3

Characters and Unicode

Total characters73263
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5188
60.1%
1.0812145600877996 3159
36.6%
0.0 198
 
2.3%
9.0 80
 
0.9%
3.0 1
 
< 0.1%

Length

2023-10-18T10:23:00.588774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-18T10:23:00.721605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5188
60.1%
1.0812145600877996 3159
36.6%
0.0 198
 
2.3%
9.0 80
 
0.9%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 15142
20.7%
1 14665
20.0%
. 8626
11.8%
9 6398
8.7%
8 6318
8.6%
6 6318
8.6%
7 6318
8.6%
2 3159
 
4.3%
4 3159
 
4.3%
5 3159
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 64637
88.2%
Other Punctuation 8626
 
11.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15142
23.4%
1 14665
22.7%
9 6398
9.9%
8 6318
9.8%
6 6318
9.8%
7 6318
9.8%
2 3159
 
4.9%
4 3159
 
4.9%
5 3159
 
4.9%
3 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 8626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73263
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15142
20.7%
1 14665
20.0%
. 8626
11.8%
9 6398
8.7%
8 6318
8.6%
6 6318
8.6%
7 6318
8.6%
2 3159
 
4.3%
4 3159
 
4.3%
5 3159
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73263
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15142
20.7%
1 14665
20.0%
. 8626
11.8%
9 6398
8.7%
8 6318
8.6%
6 6318
8.6%
7 6318
8.6%
2 3159
 
4.3%
4 3159
 
4.3%
5 3159
 
4.3%

HEARING
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
1.0
5429 
1.314150804190645
1849 
0.0
961 
9.0
 
386
3.0
 
1

Length

Max length17
Median length3
Mean length6.0009274
Min length3

Characters and Unicode

Total characters51764
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 5429
62.9%
1.314150804190645 1849
 
21.4%
0.0 961
 
11.1%
9.0 386
 
4.5%
3.0 1
 
< 0.1%

Length

2023-10-18T10:23:00.837994image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-18T10:23:00.954111image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 5429
62.9%
1.314150804190645 1849
 
21.4%
0.0 961
 
11.1%
9.0 386
 
4.5%
3.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 13285
25.7%
1 12825
24.8%
. 8626
16.7%
4 5547
10.7%
5 3698
 
7.1%
9 2235
 
4.3%
3 1850
 
3.6%
8 1849
 
3.6%
6 1849
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43138
83.3%
Other Punctuation 8626
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13285
30.8%
1 12825
29.7%
4 5547
12.9%
5 3698
 
8.6%
9 2235
 
5.2%
3 1850
 
4.3%
8 1849
 
4.3%
6 1849
 
4.3%
Other Punctuation
ValueCountFrequency (%)
. 8626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51764
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13285
25.7%
1 12825
24.8%
. 8626
16.7%
4 5547
10.7%
5 3698
 
7.1%
9 2235
 
4.3%
3 1850
 
3.6%
8 1849
 
3.6%
6 1849
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51764
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13285
25.7%
1 12825
24.8%
. 8626
16.7%
4 5547
10.7%
5 3698
 
7.1%
9 2235
 
4.3%
3 1850
 
3.6%
8 1849
 
3.6%
6 1849
 
3.6%

HEARAID
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
0.0
5577 
0.1991725768321513
1858 
1.0
1171 
9.0
 
19
6.0
 
1

Length

Max length18
Median length3
Mean length6.2309297
Min length3

Characters and Unicode

Total characters53748
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 5577
64.7%
0.1991725768321513 1858
 
21.5%
1.0 1171
 
13.6%
9.0 19
 
0.2%
6.0 1
 
< 0.1%

Length

2023-10-18T10:23:01.071834image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-10-18T10:23:01.192021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
0.0 5577
64.7%
0.1991725768321513 1858
 
21.5%
1.0 1171
 
13.6%
9.0 19
 
0.2%
6.0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 14203
26.4%
. 8626
16.0%
1 8603
16.0%
9 3735
 
6.9%
7 3716
 
6.9%
2 3716
 
6.9%
5 3716
 
6.9%
3 3716
 
6.9%
6 1859
 
3.5%
8 1858
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 45122
84.0%
Other Punctuation 8626
 
16.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14203
31.5%
1 8603
19.1%
9 3735
 
8.3%
7 3716
 
8.2%
2 3716
 
8.2%
5 3716
 
8.2%
3 3716
 
8.2%
6 1859
 
4.1%
8 1858
 
4.1%
Other Punctuation
ValueCountFrequency (%)
. 8626
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53748
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14203
26.4%
. 8626
16.0%
1 8603
16.0%
9 3735
 
6.9%
7 3716
 
6.9%
2 3716
 
6.9%
5 3716
 
6.9%
3 3716
 
6.9%
6 1859
 
3.5%
8 1858
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53748
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14203
26.4%
. 8626
16.0%
1 8603
16.0%
9 3735
 
6.9%
7 3716
 
6.9%
2 3716
 
6.9%
5 3716
 
6.9%
3 3716
 
6.9%
6 1859
 
3.5%
8 1858
 
3.5%
Distinct1378
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2023-10-18T10:23:01.406795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters69008
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique131 ?
Unique (%)1.5%

Sample

1st rowOAS30001
2nd rowOAS30001
3rd rowOAS30001
4th rowOAS30001
5th rowOAS30001
ValueCountFrequency (%)
oas30446 32
 
0.4%
oas30936 31
 
0.4%
oas30675 30
 
0.3%
oas30393 30
 
0.3%
oas31155 28
 
0.3%
oas30194 28
 
0.3%
oas30314 26
 
0.3%
oas31160 25
 
0.3%
oas31100 25
 
0.3%
oas30825 24
 
0.3%
Other values (1368) 8347
96.8%
2023-10-18T10:23:01.755122image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11332
16.4%
0 9732
14.1%
O 8626
12.5%
A 8626
12.5%
S 8626
12.5%
1 4917
7.1%
2 2658
 
3.9%
7 2585
 
3.7%
4 2524
 
3.7%
5 2518
 
3.6%
Other values (3) 6864
9.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43130
62.5%
Uppercase Letter 25878
37.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11332
26.3%
0 9732
22.6%
1 4917
11.4%
2 2658
 
6.2%
7 2585
 
6.0%
4 2524
 
5.9%
5 2518
 
5.8%
8 2345
 
5.4%
6 2315
 
5.4%
9 2204
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
O 8626
33.3%
A 8626
33.3%
S 8626
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43130
62.5%
Latin 25878
37.5%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11332
26.3%
0 9732
22.6%
1 4917
11.4%
2 2658
 
6.2%
7 2585
 
6.0%
4 2524
 
5.9%
5 2518
 
5.8%
8 2345
 
5.4%
6 2315
 
5.4%
9 2204
 
5.1%
Latin
ValueCountFrequency (%)
O 8626
33.3%
A 8626
33.3%
S 8626
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11332
16.4%
0 9732
14.1%
O 8626
12.5%
A 8626
12.5%
S 8626
12.5%
1 4917
7.1%
2 2658
 
3.9%
7 2585
 
3.7%
4 2524
 
3.7%
5 2518
 
3.6%
Other values (3) 6864
9.9%
Distinct3426
Distinct (%)39.7%
Missing0
Missing (%)0.0%
Memory size67.5 KiB
2023-10-18T10:23:01.993243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters43130
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1806 ?
Unique (%)20.9%

Sample

1st rowd0000
2nd rowd0339
3rd rowd0722
4th rowd1106
5th rowd1456
ValueCountFrequency (%)
d0000 1355
 
15.7%
d0371 26
 
0.3%
d0364 19
 
0.2%
d0385 18
 
0.2%
d0378 18
 
0.2%
d0357 14
 
0.2%
d1099 14
 
0.2%
d0406 14
 
0.2%
d0350 13
 
0.2%
d0392 13
 
0.2%
Other values (3416) 7122
82.6%
2023-10-18T10:23:02.319596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9132
21.2%
d 8601
19.9%
1 4310
10.0%
2 3377
 
7.8%
3 3332
 
7.7%
4 3051
 
7.1%
5 2507
 
5.8%
7 2418
 
5.6%
8 2246
 
5.2%
6 2155
 
5.0%
Other values (2) 2001
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 34525
80.0%
Lowercase Letter 8601
 
19.9%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 9132
26.5%
1 4310
12.5%
2 3377
 
9.8%
3 3332
 
9.7%
4 3051
 
8.8%
5 2507
 
7.3%
7 2418
 
7.0%
8 2246
 
6.5%
6 2155
 
6.2%
9 1997
 
5.8%
Lowercase Letter
ValueCountFrequency (%)
d 8601
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34529
80.1%
Latin 8601
 
19.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9132
26.4%
1 4310
12.5%
2 3377
 
9.8%
3 3332
 
9.6%
4 3051
 
8.8%
5 2507
 
7.3%
7 2418
 
7.0%
8 2246
 
6.5%
6 2155
 
6.2%
9 1997
 
5.8%
Latin
ValueCountFrequency (%)
d 8601
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43130
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9132
21.2%
d 8601
19.9%
1 4310
10.0%
2 3377
 
7.8%
3 3332
 
7.7%
4 3051
 
7.1%
5 2507
 
5.8%
7 2418
 
5.6%
8 2246
 
5.2%
6 2155
 
5.0%
Other values (2) 2001
 
4.6%

Interactions

2023-10-18T10:22:55.356790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.171899image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.003440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.881189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.673900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.489062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:54.348503image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.488405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.309777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.129501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.992060image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.789978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.601494image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:54.671238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.611763image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.442638image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.263700image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.106583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.908540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.738628image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:54.808205image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.705262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.559183image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.372295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.223691image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.026101image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.857235image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:54.924170image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.826129image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.673944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.525120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.358561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.141923image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.974765image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.026508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.960718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.781878image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.645580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.457913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.259623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:54.095025image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.126949image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:56.070917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:50.890448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:51.757405image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:52.579444image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:53.374592image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:54.207940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-10-18T10:22:55.240137image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-10-18T10:23:02.434930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
days_to_visitage at visitWEIGHTHEIGHTBPSYSBPDIASHRATEVISIONVISCORRVISWCORRHEARINGHEARAID
days_to_visit1.0000.368-0.050-0.004-0.022-0.0720.0230.0470.0420.0320.0640.061
age at visit0.3681.000-0.152-0.0510.117-0.140-0.0410.0270.0300.0340.1230.119
WEIGHT-0.050-0.1521.0000.4620.1110.173-0.0150.1180.1510.0760.1170.142
HEIGHT-0.004-0.0510.4621.0000.0690.070-0.0230.0000.0000.0000.0000.000
BPSYS-0.0220.1170.1110.0691.0000.4940.0320.0560.1280.0000.0360.111
BPDIAS-0.072-0.1400.1730.0700.4941.0000.0850.0770.2520.0000.0400.216
HRATE0.023-0.041-0.015-0.0230.0320.0851.0000.0570.1280.0000.0150.109
VISION0.0470.0270.1180.0000.0560.0770.0571.0000.7270.3650.5240.502
VISCORR0.0420.0300.1510.0000.1280.2520.1280.7271.0000.4820.5010.531
VISWCORR0.0320.0340.0760.0000.0000.0000.0000.3650.4821.0000.3450.343
HEARING0.0640.1230.1170.0000.0360.0400.0150.5240.5010.3451.0000.795
HEARAID0.0610.1190.1420.0000.1110.2160.1090.5020.5310.3430.7951.000

Missing values

2023-10-18T10:22:56.238468image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-10-18T10:22:56.511817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

OASISIDOASIS_session_labeldays_to_visitage at visitWEIGHTHEIGHTBPSYSBPDIASHRATEVISIONVISCORRVISWCORRHEARINGHEARAIDOASIS_session_label_first8OASIS_session_label_last5
0OAS30001OAS30001_UDSb1_d00000.065.19999.000000999.0138.070.072.01.01.01.01.00.0OAS30001d0000
1OAS30001OAS30001_UDSb1_d0339339.066.12155.00000064.0138.072.078.00.01.01.01.00.0OAS30001d0339
2OAS30001OAS30001_UDSb1_d0722722.067.17162.00000064.0144.080.060.00.01.01.01.00.0OAS30001d0722
3OAS30001OAS30001_UDSb1_d11061106.068.22167.00000063.5130.082.068.00.01.01.01.00.0OAS30001d1106
4OAS30001OAS30001_UDSb1_d14561456.069.18173.00000063.5142.070.072.01.01.01.01.00.0OAS30001d1456
5OAS30001OAS30001_UDSb1_d18941894.070.38177.00000063.0126.076.068.00.01.01.01.00.0OAS30001d1894
6OAS30001OAS30001_UDSb1_d21812181.071.17180.00000063.0124.064.064.00.01.01.01.00.0OAS30001d2181
7OAS30001OAS30001_UDSb1_d26992699.072.59184.19995163.5140.078.064.01.01.01.01.00.0OAS30001d2699
8OAS30001OAS30001_UDSb1_d30253025.073.48180.00000063.5114.070.068.00.01.01.01.00.0OAS30001d3025
9OAS30001OAS30001_UDSb1_d33323332.074.32185.00000063.0164.090.058.01.01.01.01.00.0OAS30001d3332
OASISIDOASIS_session_labeldays_to_visitage at visitWEIGHTHEIGHTBPSYSBPDIASHRATEVISIONVISCORRVISWCORRHEARINGHEARAIDOASIS_session_label_first8OASIS_session_label_last5
8616OAS31470OAS31470_UDSb1_d0419419.066.06174.42991270.693765130.63416274.25671871.4811590.8471530.8347111.0812151.3141510.199173OAS31470d0419
8617OAS31471OAS31471_UDSb1_d00000.065.30295.19995179.800000132.00000078.00000054.0000001.0000001.0000001.0000001.0000000.000000OAS31471d0000
8618OAS31471OAS31471_UDSb1_d0457457.066.55174.42991270.693765130.63416274.25671871.4811590.8471530.8347111.0812151.3141510.199173OAS31471d0457
8619OAS31472OAS31472_UDSb1_d00000.067.27122.59997663.500000100.00000060.00000060.0000001.0000001.0000001.0000001.0000000.000000OAS31472d0000
8620OAS31472OAS31472_UDSb1_d0482482.068.59174.42991270.693765130.63416274.25671871.4811590.8471530.8347111.0812151.3141510.199173OAS31472d0482
8621OAS31472OAS31472_UDSb1_d0826826.069.53174.42991270.693765130.63416274.25671871.4811590.8471530.8347111.0812151.3141510.199173OAS31472d0826
8622OAS31473OAS31473_UDSb1_d00000.056.61219.00000076.500000132.00000076.00000064.0000001.0000001.0000001.0000001.0000000.000000OAS31473d0000
8623OAS31473OAS31473_UDSb1_d11421142.059.74174.42991270.693765130.63416274.25671871.4811590.8471530.8347111.0812151.3141510.199173OAS31473d1142
8624OAS31474OAS31474_UDSb1_d00000.081.84151.00000063.000000140.00000080.00000068.0000001.0000001.0000001.0000001.0000000.000000OAS31474d0000
8625OAS31474OAS31474_UDSb1_d0732732.083.85150.00000062.000000138.00000062.00000060.0000001.0000001.0000001.0000001.0000000.000000OAS31474d0732